Comparison of Centralized Multi-Sensor Measurement and State Fusion Methods with an Adaptive Unscented Kalman Filter for Process Fault diagnosis

Mosallaei, M.; Salahshoor, Karim

doi:10.1109/iciafs.2008.4784007

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…Some centralised fusion estimation problem for multisensor systems were presented in [1,2], and the centralised fusion quadratic estimation problem for multisensor systems was addressed in [3]. Currently, there existed two commonly used centralised multisensor data fusion methods with adaptive unscented Kalman filter (UKF) [4] or ensemble Kalman filter [5] for process fault diagnosis including centralised measurement fusion and centralised state-vector fusion. In literature [6], Ababnah and Natarajan researched the sensor deployment problem in a centralised data fusion based a distributed sensor network detection system.…”

Section: Introductionmentioning

confidence: 99%

Comparison of centralised scaled unscented Kalman filter and extended Kalman filter for multisensor data fusion architectures

Xing

Xia

2016

IET signal process.

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This study presents three non-linear centralised scaled unscented Kalman filter (SUKF) for multisensor data fusion algorithms, which are augmented measurements, measurements weighted and sequential filtering fusion. First, the accuracy analysis of extended Kalman filter (EKF) and SUKF is investigated in detail. Second, through comparing the error covariance traces and the absolute mean estimation errors of X and Y directions of centralised SUKF for multisensor data fusion algorithms with that of centralised EKF for multisensor data fusion algorithms, it can be remarked that the performance of centralised augmented measurements SUKF for multisensor data fusion algorithm is the best one among the six algorithms, which is to say that Algorithm (Iu) shows the best performance in accuracy. Finally, combining and synthetically analysing the running time of six algorithms, it illustrates that Algorithm (Iu) is optimal in comprehensive aspects among six algorithms.

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Section: Introductionmentioning

confidence: 99%

Comparison of centralised scaled unscented Kalman filter and extended Kalman filter for multisensor data fusion architectures

Xing

Xia

2016

IET signal process.

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“…Mosallaei and Salahshoor investigate the application of centralized multisensor data fusion (CMSDF) technique to enhance the process fault detection. The measurement fusion methods directly fuse observations or sensor measurements to obtain a weighted or combined measurement and then use a single Kalman filter to obtain the final state estimate based upon the fused measurement [12]. These authors presented a Particle Filter (PF) based multisensor data fusion (MSDF) technique in an integrated Navigation and Guidance System (NGS) design based on low-cost avionics sensors [13].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fusion Design Using Multiscale Unscented Kalman Filter Approach for Multisensor Data Fusion

Wang

Dong

2015

Mathematical Problems in Engineering

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In order to improve the reliability of measurement data, the multisensor data fusion technology has progressed greatly in improving the accuracy of measurement data. This paper utilizes the real-time, recursive, and optimal estimation characteristics of unscented Kalman filter (UKF), as well as the unique advantages of multiscale wavelet transform decomposition in data analysis to effectively integrate observational data from multiple sensors. A new multiscale UKF-based multisensor data fusion algorithm is proposed by combining the UKF with multiscale signal analysis. Firstly, model-based UKF is introduced into the multiple sensors, and then the model is decomposed at multiple scales onto the coarse scale with wavelets. Next, signals decomposed from fine to coarse scales are adjusted using the denoised observational data from corresponding sensors and reconstructed with wavelets to obtain the fused signals. Finally, the processed data are fused using adaptive weighted fusion algorithm. Comparison of simulation and experimental results shows that the proposed method can effectively improve the antijamming capability of the measurement system and ensure the reliability and accuracy of sensor measurement system compared to the use of data fusion algorithm alone.

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“…[36] presents a State-Vector Fusion (SVF) method in which each measurement is processed by its own local filter simultaneously. Then, the updated estimated states and the predicted covariances are fused together [37]. The Measurement Fusion (MF) method, introduced in Ref.…”

Section: Optimal State Estimation and Navigationmentioning

confidence: 99%

“…Both SVF and MF methods require less computational load compared to the standard KF [38]. However, the derivation of these fusion methods assumes uncorrelated measurement noise for multisensor systems [37,40]. In most of the multisensor systems, the sensors' noises are correlated due to the interference signal between sensors [40].…”

Section: Optimal State Estimation and Navigationmentioning

confidence: 99%

Algorithms for spacecraft formation flying navigation based on wireless positioning system measurements

Goh¹

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Comparison of Centralized Multi-Sensor Measurement and State Fusion Methods with an Adaptive Unscented Kalman Filter for Process Fault diagnosis

Cited by 5 publications

References 15 publications

Comparison of centralised scaled unscented Kalman filter and extended Kalman filter for multisensor data fusion architectures

Comparison of centralised scaled unscented Kalman filter and extended Kalman filter for multisensor data fusion architectures

Adaptive Fusion Design Using Multiscale Unscented Kalman Filter Approach for Multisensor Data Fusion

Algorithms for spacecraft formation flying navigation based on wireless positioning system measurements

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